OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
Copied to clipboard
| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
Similar Papers
AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for retrieving proper knowledge relevant to conversational context use only KG structure . empirical evaluations present a marked performance improvement of AttnIO compared to all baselines in OpenDialKG dataset . |
| Approach: | They propose a dialog-conditioned path traversal model that makes full use of rich structural information in KG . they show a marked performance improvement compared to baselines in OpenDialKG a KG dataset . |
| Outcome: | The proposed model makes full use of rich structural information in KG structure . it can be trained to generate an adequate knowledge path even when paths are not available . |
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems that explore user preference through conversational interactions do not exploit the context and knowledge to make accurate recommendations. |
| Approach: | They propose a model that performs tree-structured reasoning on a knowledge graph and generates informative dialog acts to guide language generation. |
| Outcome: | The proposed model can arrive at more accurate recommendation and generate more informative and engaging responses. |
C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots . |
| Approach: | They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information. |
| Outcome: | The proposed graph incorporates social commonsense knowledge and dialog flow information. |
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing systems that require extensive labor to process user requests are limited in their reasoning capabilities and require extensive manual effort to design. |
| Approach: | They propose a method that allows a transformer model to walk on a large-scale knowledge graph to generate responses by reasoning over differentiable knowledge graphs. |
| Outcome: | The proposed method allows a transformer model to walk on a large-scale knowledge graph to generate responses. |
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. |
| Approach: | They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph. |
| Outcome: | The proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a Knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. |
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs (D19-1)
Copied to clipboard
| Challenge: | Existing work has not shown that knowledge-grounded models can zero-shot adapt to updated, unseen knowledge graphs. |
| Approach: | They propose a task to apply dynamic knowledge graphs to neural conversation models . they propose 'dyKgChat' that selects an output from two networks at each time step . |
| Outcome: | The proposed model outperforms existing knowledge-grounded conversation models in evaluation metrics. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |
GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems (2020.emnlp-main)
Copied to clipboard
| Challenge: | End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs. |
| Approach: | They propose a recurrent cell architecture which exploits the structural information in dialogue history . they propose recursive cell architecture to allow representation learning on graphs . |
| Outcome: | The proposed model improves on two different datasets on task-oriented dialogues. |
CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing conversational recommender systems (CRS) do not track the deep shift of user interest in conversations due to the complex of high-order and incomplete paths. |
| Approach: | They propose a conversational context-based reinforcement learning model which does explicit multi-hop reasoning on KGs with a contextual context-driven reinforcement learning framework. |
| Outcome: | Extensive experiments show that CRFR improves on paths of interest shift in knowledge graphs (KGs) . |
Conversational Semantic Parsing using Dynamic Context Graphs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on conversational semantic parsing has focused on answering questions in isolation . whereas existing work on KBQA is focused on resolving questions in the context of natural language questions . |
| Approach: | They propose to model conversational semantic parsing over general purpose knowledge graphs with millions of entities and thousands of relation-types by exploiting its underlying structure and encoding it with a graph neural network. |
| Outcome: | The proposed model is better at processing discourse information and longer interactions . it is better than static models at handling ellipsis and coreference, the authors show . |